Semantic Cache

The Problem

Exact-match caches break down in semantic workloads. Queries that mean the same thing rarely share the same key, and once you move beyond toy in-memory demos the hard part stops being nearest-neighbor lookup and becomes cache policy: reuse, coverage, freshness, promotion, eviction, and cost control across vector and object storage tiers.

Approach

Semantic Cache is a tiered semantic/object caching system with vector and object storage, built for workloads where semantic reuse has to behave like a real cache instead of an ANN demo. The design centers on three operating modes: similarity for direct reuse, dissimilarity for coverage and anti-clustering, and a hybrid mode that balances both.

The long-term architecture is a custom L0 hot path, a LanceDB-backed warm tier, and colder distributed persistence behind a single policy layer. The goal is simple: reduce origin calls for semantically equivalent requests without letting the cache collapse into a pile of near-duplicate answers.

Direction

The project is headed toward a semantic cache that can scale by tier instead of pretending one storage engine should do every job. Threshold selection, invalidation, admission, eviction, and observability are all treated as first-class problems, because that is where semantic caching either becomes operationally credible or falls apart.

Key Design Decisions

Three operating modes. Similarity-only, dissimilarity-only, and hybrid retrieval serve different goals: hit rate, semantic coverage, or both. The system is designed to make that tradeoff explicit instead of forcing every workload through one retrieval strategy.

Tiered persistence instead of a single vector store. The design target is a custom L0 for the hot set, LanceDB for the warm tier, and pluggable colder backends for longer-lived or distributed persistence. That keeps the fast path tight without capping the total cacheable space.

Adaptive thresholds and category-aware policy. A single global similarity threshold is a good way to either miss useful reuse or serve garbage. Thresholds, TTLs, and quota behavior should move with query category and observed traffic patterns instead of staying fixed forever.

Freshness is proactive. TTL remains a safety net, but mutable knowledge bases need source-aware invalidation and coordinated purge behavior across tiers. Freshness cannot depend entirely on waiting for entries to age out.

Cache semantics matter as much as vector search. Admission, eviction, anti-clustering, warming, observability, and bindings are part of the product surface. A semantic cache that only does retrieval is still missing the operational half of the problem.